Yes, Context Rot generally worsens over time as more content is added to the conversation or prompt. The longer the interaction runs without pruning, summarization, or restructuring, the more diluted important information becomes. This is not a sudden failure but a gradual degradation, which makes it harder to detect and diagnose.
Each new turn introduces additional tokens that compete for the model’s attention. Over time, this pushes earlier instructions, definitions, and constraints further back in the context. Even if those early tokens are still present, they are less influential compared to newer text. This is why long-running agents often show a steady decline in precision or task focus.
To slow or reverse this trend, production systems usually reset or compress context periodically. Summarizing earlier turns into a compact state description or retrieving fresh context from a vector database such as Milvus or Zilliz Cloud helps maintain relevance. Without these measures, Context Rot will almost always intensify as conversations grow longer.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md